ABSTRACT
The past 20 years witnessed an expansion of power spectrum estimation techniques, which have proved essential in many applications, such as communications, sonar, radar, speech/image processing, geophysics, and biomedical signal processing [1-3]. In power spectrum estimation, the process under consideration is treated as a superposition of statistically uncorrelated harmonic components. The distribution of power among these frequency components is the power spectrum. As such phase relations between frequency components are suppressed. The information in the power spectrum is essentially present in the autocorrelation sequence, which would suffice for the complete statistical description of a Gaussian process of known mean. However, there are applications where one would need to obtain information regarding deviations from the Gaussianity assumption and presence of nonlinearities. In these cases power spectrum is of little help, and a look beyond the power spectrum or autocorrelation domain is needed. Higher-order spectra (HOS) (of order greater than two), which are defined in terms of higher-order cumulants of the data, do contain such information [4]. The third order spectrum is commonly referred to as bispectrum and the fourth-order as trispectrum. The power spectrum is also a member of the higher-order spectral class; it is the second-order spectrum.
